Article Dans Une Revue IEEE Transactions on Automatic Control Année : 2017

Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing

Résumé

A new procedure to design parameter estimators with enhanced performance is proposed in the technical note. For classical linear regression forms, it yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation. The technique is also applied to nonlinear regressions with “partially” monotonic parameter dependence-giving rise again to estimators with enhanced performance. Simulation results illustrate the advantages of the proposed procedure in both scenarios.

Fichier principal
Vignette du fichier
hal-01612256 Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing.pdf (443.14 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

hal-01612256 , version 1 (17-06-2020)

Licence

Identifiants

Citer

Stanislav Aranovskiy, Alexey Bobtsov, Romeo Ortega, Anton Pyrkin. Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing. IEEE Transactions on Automatic Control, 2017, 62 (7), pp.3546 - 3550. ⟨10.1109/TAC.2016.2614889⟩. ⟨hal-01612256⟩
374 Consultations
568 Téléchargements

Altmetric

Partager

  • More